Utility driven adaptive workflow execution
Lee, K., Paton, N.W., Sakellariou, R. and Fernandes, A.A.A. (2009) Utility driven adaptive workflow execution. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID 2009), 18 - 21 May, Shanghai, China.
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Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has responsibility for establishing how best to map tasks within a workflow to the available resources. As workflows are typically run over shared resources, and thus face unpredictable and changing resource capabilties, there may be benefit to be derived from adapting the task-to-resource mapping while a workflow is executing. This paper describes the use of utility functions to express the relative merits of alternative mappings; in essence, a utility function can be used to give a score to a candidate mapping, and the exploration of alternative mappings can be cast as an optimization problem. In this approach, changing the utility function allows adaptations to be carried out with a view to meeting different objectives. The contributions of this paper include: (i) a description of how adaptive workflow execution can be expressed as an optimization problem where the objective of the adaptation is to maximize some property expressed as a utility function; (ii) a description of how the approach has been applied to support adaptive workflow execution in grids; and (iii) an experimental evaluation of the resulting approach for alternative utility measures based on response time and profit.
|Publication Type:||Conference Paper|
|Copyright:||© 2010 IEEE|
|Notes:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This paper appears in: Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID 2009), pp 220-227|
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